A previous patent application of the present inventors, U.S. Ser. No. 13/835,909 filed Mar. 15, 2013, describes classification of mass spectrometry data of blood-based samples to predict cancer patient benefit from yeast-based immunotherapy, including GI-4000, a drug developed by GlobeImmune, Inc., Louisville Colo. The entire content of the '909 application is incorporated by reference herein. The description of the Deep MALDI mass spectrometry methods in that document, as well as U.S. Ser. No. 13/836,436 filed Mar. 15, 2013, also incorporated by reference herein. The interested reader is specifically directed to that section of the '909 application and the '436 application for reference.
Briefly, GI-4000 is a yeast based immunotherapy targeted at RAS mutations common in pancreatic cancer. GlobeImmune conducted a Phase II study to evaluate the efficacy of this treatment in combination with gemcitabine compared to gemcitabine alone in the adjuvant setting. While the overall result was ambiguous, there were hints of benefit from GI-4000 in some subgroups. Detailed analysis of follow-up data also showed that GI-4000 did stimulate yeast-specific immune response in some patients.
The inventors' assignee, Biodesix, Inc. (Boulder Colo.) has developed advanced mass spectrometry analysis techniques which, in combination with sophisticated data analysis algorithms and novel learning theory approaches, enable the development of predictive assays from serum or plasma samples. These techniques have led to the development of a commercially available assay, VeriStrat®, clinically used in the prediction of erlotinib resistance in second line non-small cell lung cancer from pre-treatment samples. The VeriStrat test is described at length in U.S. Pat. No. 7,736,905, the content of which is incorporated by reference herein.
We applied the Biodesix assay development platform to samples from the GI-4000 trial to develop a test to select patients who would benefit from the addition of GI-4000 to gemcitabine in the adjuvant treatment of pancreatic cancer. While previous attempts at this problem showed promise, performance estimates were limited to cross-validation results due to the small size of the available sample set.
A new classifier generation method was developed as explained in this document. As explained below, using newly developed training algorithms we were able to split the available samples into proper training and test sets. This greatly enhances our confidence in the generalizability of the development results. This document, in Example 1, describes the results and methods used in the development of a predictive test for patient benefit for GI-4000+gemcitabine as an example of the generation and use of the classifier development methodology described herein.
A further example of development of a classifier and method for predicting patient benefit from anti-cancer drugs is also described. This example is in the context of non-small cell lung cancer (NSCLC), epidermal growth factor receptor inhibitors (EGFR-Is) and chemotherapy drugs.
A further example is described in which a classifier is generated from genomic data, in this example messenger RNA (mRNA) transcript expression levels from tumor samples from humans with breast cancer. The classifier is predictive of whether a breast cancer patient is at risk of early relapse.
However, as will be appreciated from the following discussion, the methodology is of general applicability to classification problems, especially those where p>n and the following detailed descriptions are offered by way of example and not limitation.